ID 原文 译文
58748 该方案首先在同一版图上采用不同虚拟孔配置实现基本单元异或门/与门逻辑功能,并提取逻辑混淆单元的特征信息制作标准单元库; First, different virtual hole configurations are adopted to realize XOR/AND logical functions on the same layout, and feature information of the logical obfuscating circuit is extracted to make the standard cell physical library.
58749 然后利用随机插入算法将混淆标准单元电路应用于电路网表; Then, the obfuscation physical library is applied in the circuit netlist by the random insertion algorithm.
58750 最后采用基准电路验证所提方案的有效性。 Finally, the ISCAS benchmark is used to verify the effectiveness of the proposed scheme.
58751 仿真结果显示,Reed-Muller逻辑伪装门比标准单元库的版图相似度提高了14.36%,而较大规模测试电路功耗额外开销仅为2.36%。 Simulation results reveal that the similarity of the Reed-Muller logic camouflage layout is improved by 14. 36%, and that the power consumption overhead is about 2. 36% under the larger scale benchmark.
58752 仿真结果表明,所设计的伪装门可以有效地防御逆向工程,提高了电路的硬件安全。 Experiment indicates that the designed obfuscation gate can effectively resist reverse engineering and improve the hardware security of the circuit.
58753 由于低空小型无人机类的动态小目标视觉特征不明显,且在检测过程中尺度可能变化较大,故传统的检测算法在检测该类目标时易受到干扰,检测速度和稳定性较差。 The visual characteristics of low-altitude drones are less obvious and the scale changes during the detection process. Traditional detection methods are susceptible to interference during detection, and most of those methods cannot work quickly and robustly.
58754 针对此问题,提出了一种结合YOLOv3改进模型和超分辨率重建技术的无人机实时检测算法。 To solve this problem, a real-time drone detection algorithm combined with the improved YOLOv3 model and the super resolution method is proposed in this paper.
58755 首先以三帧间差分法筛选可疑区域; First, frame difference is used to propose the candidate area, and the super-resolution method is used to strengthen the details.
58756 然后使用轻量级卷积神经网络进行可疑区域的超分辨率重建,增强细节信息; Then the dimensional clustering algorithm is used to regenerate the anchors for the model, and the model is slightly adjusted.
58757 再用维度聚类算法重新生成YOLOv3模型的预选框参数并调整预选框分配,使用改进模型扫描全图和可疑区域,进行无人机检测;在视频流检测中,将帧间关系作为依据,强化选定区域的细节特征后再进行目标检测,实现无人机的检测式追踪。 Finally, we use the improved YOLOv3 to scan both the whole frame and the processed candidate area so as to detect the drones. The frame relationship is also used to implement tracking of drones by real-time detection.